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{\Large \bfseries  Abstract}   \end{center}  
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\begin{abstract} 

\thispagestyle{empty}Distributed information processing has attract
more interest recently because its advantages in distributed network
than centralized method. One of the tools of distributed signal processing
is the distributed consensus algorithm. If the consensus algorithm
is to find the average value over the network, the algorithm is called
the distributed average consensus (DAC), which is an matrix iterative
algorithm to find the dominant eigenvector of the matrix. Generally,
the algorithm is required to return the result more quickly so that
the distributed system can have a higher data processing performance.
Therefore, many efforts have been devoted into the optimization of
the distributed average algorithm. However, most of the optimization
are centralized method so the system can not be optimized in a distributed
network. In addition, the existing distributed optimization algorithm
converges very slowly. 

Consequently, we proposed a distributed real-time optimization for
the DAC algorithms. The optimization has advantages in low computation
time and can work simultaneously with the consensus algorithm. Later,
an application of cloud detection is given where optimized DAC algorithms
are applied to perform the hypothesis testing. Simulation result shows
that the DAC algorithm using centralized optimization and proposed
optimization have similar performances in convergence rate, when floating
point number with double format is used and the network size is less
than 32. In addition, the performances of the detection system with
different number of sensors are plotted using relative operating characteristic
curves. It is shown that the detection system with more sensors can
have better performance. 



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